This document describes the replication files associated with the paper:
Hirofumi Miwa, Reiko Arami, and Masaki Taniguchi. “Detecting Voter Understanding of Ideological Labels Using a Conjoint Experiment.” Political Behavior.

The following files are contained herein:
0_data_preparation.R (R code to generate datasets)
1_general_understanding.R (R code to replicate analyses reported in the Section "General Understanding of Left–Right Labels" and Online Appendix F.1.1)
2_understanding_conditioned_by_age.R (R code to replicate analyses reported in the Section "Left–Right Understanding Conditioned by Age" and Online Appendices F.1.2 and F.2)
3_latent_groups.R (R code to replicate analyses reported in the Section "Latent Groups on Understanding Left–Right Labels" and Online Appendix F.3)
a_balance_check.R (R code to replicate analyses reported in Online Appendix E)
b_analyses_including_satisficing_responses.R (R code to replicate analyses reported in Online Appendix G.1)
c_heterogeneous_effects.R (R code to replicate analyses reported in Online Appendix G.2)
d_interaction_between_attributes.R (R code to replicate analyses reported in Online Appendix G.3)
e_correlational_approach.R (R code to replicate analyses reported in Online Appendix G.4)
f_birth_cohort_and_issue_familiatiry.R (R code to replicate analyses reported in Online Appendix B)
finite_mixture_linear_regressions.cpp (C++ code implementing a Gibbs sampler for the finite mixture model of linear regressions)
ideological_label_conjoint_data.csv (the original survey data)
prefecture_DID_ratio.csv (the densely inhabited district population ratio of prefectures)
respondent_data.csv (respondent-level dataset)
task_raw_data.csv (task-level dataset including satisficing responses)
task_data.csv (task-level dataset excluding satisficing responses)

Each replication codes work independently from other replication codes. Please open 0_data_preparation.R with UTF-8. This file contains Japanese characters and will get garbled when opened with another encoding.

If you want to skip the estimation process via MCMC to save time, please contact the corresponding author. The corresponding author will share Rdata files containing the MCMC results with you in some way.

All data analyses using these replication files were carried out using R version 3.6.2 as well as the following R packages:  coda version 0.19-3, dichromat version 2.0.0, estimatr version 0.22.0, lfe version 2.8–5, Rcpp version 1.0.3, and RcppArmadillo version 0.9.850.1.0.

If you have any questions or concerns with the files, please feel free to contact the corresponding author.